Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network.

CueT is a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a-priori and evolve constantly.

ManiMatrix is a system that provides controls and visualizations that enable system builders to refine the behavior of classification systems in an intuitive manner. With ManiMatrix, users directly refine parameters of a confusion matrix via an interactive cycle of reclassification and visualization.

Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative.

In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. We designed and developed a new interactive visualization system that presents a graphical view of confusion matrices to help users under-stand relative merits of various classifiers.

Popular image search engines have begun to provide tags based on simple characteristics of images (such as tags for black and white images or images that contain a face), but such approaches are limited by the fact that it is unclear what tags end users want to be able to use in examining image search results. CueFlik is an image search application that allows end users to quickly create (and reuse) their own rules for re-ranking images based on their visual characteristics.

Handwritten input is inherently ambiguous, and recognition systems will always make errors. Unfortunately, work on error recovery mechanisms has mainly focused on interface innovations that allow users to manually transform the erroneous recognition result into the intended one. In our work, we propose a mixed-initiative approach to error correction. CueTIP is a novel correction interface that takes advantage of the recognizer to continually evolve its results using the additional information from user corrections. This significantly reduces the number of actions required to reach the intended result.